•Selected from a pool of 1600+ applicants for a year-long fellowship to study machine learning and address business challenges. Tasked with contributing to the Allstate Wildfire Incidence and Property Insurance: A Data-Driven Analysis project.
•Utilized Python libraries such as Pandas, NumPy, MatplotLib, and Seaborn to clean and prepare data, enabling feature engineering and deep learning model creation within a community of over 200 members.
•Collaborated with a team of 5 fellows, employing the CRISP-DM methodology to tackle complex business issues and deliver effective machine learning solutions.
•Conducted extensive evaluations and experiments using feature engineering and deep learning techniques like Neural Networks and Gradient Boosting to optimize predictive models.
